Explorative Data Analysis (Penguins)

Please interprete all results you get as well!

# Delete all variables
rm( list = ls() )

# Import libaries
library(ggplot2)
library(GGally)

## Warning: package 'GGally' was built under R version 4.1.3

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.1.3

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(plotly)

## Warning: package 'plotly' was built under R version 4.1.3

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

Import Data

Import the dataframe PenguinsWithoutMissingValues from the csv file.

Use therefore the function read.csv() or read.csv2() depending on your system’s language (for a correct “.” and “,” interpretation)

# Import the dataframe PenguinsWithoutMissingValues from the csv file.
data = read.csv("C:/Users/Dari-Laptop/Desktop/FH Karnten - Master - AppDs/StatisticsAppDSLaptop/PenguinsWithoutMissingValues.csv")

Descriptive Analysis

1. How many obserations are there?

# 1) How many obserations are there?
count(data)

There are 333 observations.

2. What are the names of the observed variables?

# 2) What are the names of the observed variables?
names(data)

## [1] "IndividualID"      "Species"           "Island"           
## [4] "CulmenLength.mm."  "CulmenDepth.mm."   "FlipperLength.mm."
## [7] "BodyMass.g."       "Gender"

The names of observed variables are:

• IndividualID
• Species
• Island
• CulmenLength(mm)
• CulmenDepth(mm)
• FlipperLength(mm)
• Bodymass(g)
• Gender

3. How many penguins per Species are there?

Use therefore the library dplyr with the functions group_by() and summarize()

# 3) How many penguins per Species are there?
data %>% count(Species)

There are:

• 146 Adele species
• 68 Chinstrap species
• 119 Gentoo species

4. How many penguins per Species and Gender are there?

Use therefore the library dplyr with the functions group_by() and summarize()

# 4) How many penguins per Species and Gender are there?
data %>% 
  group_by(Species, Gender) %>%
  summarise(n = n())

## `summarise()` has grouped output by 'Species'. You can override using the
## `.groups` argument.

There are:

• Adele
  • 73 Male species
  • 73 Female species
• Chinstrap
  • 34 Male species
  • 34 Female species
• Gentoo
  • 61 Male species
  • 58 Female species

5. BodyMass statistics per Species and Gender

Create a table with following information; Per Species and Gender:

• the amount of observations,
• minimum of BodyMass,
• median of BodyMass,
• average of BodyMass,
• maximum of BodyMass.

Use therefore the library dplyr with the functions group_by() and summarize()

# 5) BodyMass statistics per Species and Gender
# 
# Create a table with following information; 
# Per Species and Gender: 
#   
# - the amount of observations, 
# - minimum of BodyMass, 
# - median of BodyMass, 
# - average of BodyMass, 
# - maximum of BodyMass.

data %>%
  group_by(Species, Gender) %>%
  summarise(Observations = n(), 
            Minimum = min(BodyMass.g.),
            Median = median(BodyMass.g.),
            Average = mean(BodyMass.g.),
            Maximum = max(BodyMass.g.))

## `summarise()` has grouped output by 'Species'. You can override using the
## `.groups` argument.

This gives us the following results:

• Adele
  • Female species
    • 73 observations
    • 2850g minimum body mass
    • 3400g median body mass
    • 3368.836g average body mass
    • 3900g maximum body mass
  • Male species
    • 73 observations
    • 3325g minimum body mass
    • 4000g median body mass
    • 4043.493g average body mass
    • 4775g maximum body mass
• Chinstrap
  • Female species
    • 34 observations
    • 2700g minimum body mass
    • 3550g median body mass
    • 3527.206g average body mass
    • 4150g maximum body mass
  • Male species
    • 34 observations
    • 3250g minimum body mass
    • 3950g median body mass
    • 3938.971g average body mass
    • 4800g maximum body mass
• Gentoo
  • Female species
    • 58 observations
    • 3950g minimum body mass
    • 4700g median body mass
    • 4679.741g average body mass
    • 5200g maximum body mass
  • Male species
    • 61 observations
    • 4750g minimum body mass
    • 5500g median body mass
    • 5484.836g average body mass
    • 6300g maximum body mass

6. Quantitative statistics per Species and Gender?

Create a table with following information; Per Island, Species and Gender:

• the amount of observations,
• average of CulmenLength.mm.,
• average of CulmenDepth.mm.,
• average of FlipperLength.mm.,
• average of BodyMass.g.

Use therefore the library dplyr with the functions group_by() and summarize()

# 6. Quantitative statistics per Species and Gender?
#   Create a table with following information; Per Island, Species and Gender:
#   
# the amount of observations,
# average of CulmenLength.mm.,
# average of CulmenDepth.mm.,
# average of FlipperLength.mm.,
# average of BodyMass.g.

data %>%
  group_by(Island, Species, Gender) %>%
  summarise(Observations = n(), 
            CulmenLengthAvg = mean(CulmenLength.mm.),
            CulmenDepthAvg = mean(CulmenDepth.mm.),
            FlipperLengthAvg = mean(FlipperLength.mm.),
            BodyMassAvg = mean(BodyMass.g.))

## `summarise()` has grouped output by 'Island', 'Species'. You can override using
## the `.groups` argument.

This gives us the following results:

• Biscoe Island
  • Adele species
    • Female
      • 22 observations
      • 37.359mm average CulmenLength
      • 17.704mm average CulmenDepth
      • 187.181mm average FlipperLength
      • 3369.318g average BodyMass
    • Male
      • 22 observations
      • 40.590mm average CulmenLength
      • 19.036mm average CulmenDepth
      • 190.409mm average FlipperLength
      • 4050.000g average BodyMass
  • Gentoo species
    • Female
      • 58 observations
      • 45.563mm average CulmenLength
      • 14.237mm average CulmenDepth
      • 212.706mm average FlipperLength
      • 4679.741g average BodyMass
    • Male
      • 61 observations
      • 49.473mm average CulmenLength
      • 15.718mm average CulmenDepth
      • 221.541mm average FlipperLength
      • 5484.836g average BodyMass
• Dream Island
  • Adele species
    • Female
      • 27 observations
      • 36.911mm average CulmenLength
      • 17.618mm average CulmenDepth
      • 187.851mm average FlipperLength
      • 3344.444g average BodyMass
    • Male
      • 28 observations
      • 40.071mm average CulmenLength
      • 18.839mm average CulmenDepth
      • 191.928mm average FlipperLength
      • 4045.536g average BodyMass
  • Chinstrap species
    • Female
      • 34 observations
      • 46.573mm average CulmenLength
      • 17.588mm average CulmenDepth
      • 191.735mm average FlipperLength
      • 3527.206g average BodyMass
    • Male
      • 34 observations
      • 51.094mm average CulmenLength
      • 19.252mm average CulmenDepth
      • 199.911mm average FlipperLength
      • 4034.783g average BodyMass
• Torgesen Island
  • Adele species
    • Female
      • 24 observations
      • 37.554mm average CulmenLength
      • 17.550mm average CulmenDepth
      • 188.291mm average FlipperLength
      • 3395.833g average BodyMass
    • Male
      • 23 observations
      • 40.586mm average CulmenLength
      • 19.391mm average CulmenDepth
      • 194.913mm average FlipperLength
      • 40434.783g average BodyMass

7. Provide a summary statistics of the columns

# 7) Provide a summary statistics of the columns

summary(data)

##  IndividualID         Species             Island          CulmenLength.mm.
##  Length:333         Length:333         Length:333         Min.   :32.10   
##  Class :character   Class :character   Class :character   1st Qu.:39.50   
##  Mode  :character   Mode  :character   Mode  :character   Median :44.50   
##                                                           Mean   :43.99   
##                                                           3rd Qu.:48.60   
##                                                           Max.   :59.60   
##  CulmenDepth.mm. FlipperLength.mm.  BodyMass.g.      Gender         
##  Min.   :13.10   Min.   :172       Min.   :2700   Length:333        
##  1st Qu.:15.60   1st Qu.:190       1st Qu.:3550   Class :character  
##  Median :17.30   Median :197       Median :4050   Mode  :character  
##  Mean   :17.16   Mean   :201       Mean   :4207                     
##  3rd Qu.:18.70   3rd Qu.:213       3rd Qu.:4775                     
##  Max.   :21.50   Max.   :231       Max.   :6300

As we can see, summary statistics can be applied only to quatitative columns, providing us the minimum, quantiles and maximum.

Explorative Analysis

Pairs plot

• use the pairplot only for quantitative variables
• use different colors for the different Species
• use the function ggpairs from library(GGally)

## Pairs plot

ggpairs(data, columns=4:7, ggplot2::aes(colour=Species))

As we can see, there is a good correlation between:

• BodyMass and CulmenLength
• BodyMass and FlipperLength
• BodyMass and CulmenDepth

Histograms, Densities, Boxplots, and Scatterplots and Facets

Histogram

1. Use a histogram (from library ggplot) to visualize CulmenLength.mm.

ggplot(data, aes(x=CulmenLength.mm.)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

2. Use different colors for different Species

ggplot(data, aes(x=CulmenLength.mm., fill=Species)) + 
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

3. Use the facet plot for different Species

ggplot(data, aes(x=CulmenLength.mm., fill=Species)) + 
  geom_histogram() + facet_grid(.~Species)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

4. Use the facet plot for different Species and Gender

ggplot(data, aes(x=CulmenLength.mm., fill=Species)) + 
  geom_histogram() + facet_grid(Species ~ Gender)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Density

1. Use a density plot for CulmenLength.mm. for the different Species, facet_grid the Gender

ggplot(data, aes(x=CulmenLength.mm., fill=Species)) + 
  geom_density() + facet_grid(.~Gender)

2. Use a density plot for CulmenLength.mm. for the different Species, facet_grid the Island

ggplot(data, aes(x=CulmenLength.mm., fill=Species)) + 
  geom_density() + facet_grid(.~Island)

Boxplot

Use a boxplot for CulmenLength.mm. for the different Species, facet_grid the Gender

Can you spot any outliers?

ggplot(data, aes(x=CulmenLength.mm., fill=Species)) + 
  geom_boxplot() + facet_grid(.~Gender)

Yes, there are outliers for:

• Chinstrap species
  • Males
  • Females
• Gentoo species
  • Males
• Adele species
  • Males

Scatterplot

Use a scatterplot for CulmenLength.mm. and BodyMass.g.

Create

• a scatterplot for CulmenLength.mm. and BodyMass.g.
• color different Species
• facet_grid for Gender

ggplot(data, aes(x=CulmenLength.mm., y = BodyMass.g., color=Species)) + 
  geom_point() + facet_grid(.~Gender)

Interactive Plots

Create

• a scatterplot for CulmenLength.mm. and BodyMass.g.
• color the different Species
• facet_grid for Gender
• create a ggploty plot out of it
• add the information of IndividualID in the hoover-text

Can you spot any outliers? What’s the IndividualID of the potential outlier?

p = ggplot(data, aes(x=CulmenLength.mm., y = BodyMass.g., color=Species, text = paste("IndividualID :", IndividualID))) + 
  geom_point() + facet_grid(.~Gender)

ggplotly(p)

Yes, there are some outliers in our scatter plots:

• Gentoo species
  • Male ID N56A2 has a bigger CulmenLength
  • Male ID N39A2 has a bigger BodyMass
• Chinstrap species
  • Female ID N71A2 has a bigger CulmenLength
  • Female ID N72A1 has a lower BodyMass